// //

What You'll Learn

Master R Programming for Data Science and unlock the power of data. Learn to use R for data analysis, statistical modeling, and data visualization. Gain proficiency in R packages like dplyr, tidyr, ggplot2, and caret.

Course Benefits
Industry Certification

Internationally recognized qualification

Expert Instructors

Learn from industry professionals

Dedicated Support

Assistance during and after training

Practical Skills

Apply knowledge immediately

Comprehensive 10-day curriculum with all materials included
Hands-on exercises and real-world case studies
Valuable networking opportunities with peers and experts
Post-course resources and refresher materials
Training on R Programming for Data Science - Course Cover Image
Duration 10 Days
Level Intermediate
Format In-Person

Course Overview

Featured

R is one of the leading programming languages for data analysis and statistical computing, widely used in industries such as finance, healthcare, and technology. This course will provide you with skills that are in high demand. Participants will learn the fundamental principles of R, data manipulation, statistical modeling, and data visualization techniques that are crucial for data-driven decision-making. The course is designed to be hands-on, with practical exercises and real-world projects that allow participants to apply their skills immediately. By the end of the course, participants will be well-equipped to tackle data analysis challenges in various domains, enhancing their career prospects in the growing field of data science.

Duration

10 Days

Who Should Attend

This course is suitable for individuals with a basic understanding of programming and a keen interest in data science. Data analysts, data scientists, researchers, and students seeking to enhance their data analysis skills will benefit greatly from this training.

Course Impact

Organizational Impact

  • Improve efficiency by enabling employees to independently access, clean, and prepare data.

  • Support faster, data-driven decision-making through timely insights.

  • Foster a data-literate culture to uncover trends and boost profitability.

  • Standardize R programming knowledge for consistent and reliable analysis.

Personal Impact

  • Gain a highly valuable and in-demand skill in data science.

  • Advance toward senior roles in data science, analytics, or business intelligence.

  • Contribute to organizational success with actionable, data-driven insights.

  • Build confidence to lead and champion data initiatives.

Course Objectives

By the end of this course, participants will be able to:

  • Understand the R programming language and its applications in data science.
  • Perform data manipulation and transformation using R packages.
  • Implement statistical analysis and hypothesis testing in R.
  • Create compelling data visualizations to communicate findings effectively.
  • Build predictive models using machine learning techniques.
  • Develop and document R scripts for reproducible data analysis.

Course Outline

Module 1: Introduction to R Programming

  • Overview of R and its ecosystem
  • Installation and setup of R and RStudio
  • Basic R syntax and data types
  • Introduction to R packages

Module 2: Data Manipulation with dplyr

  • Importing and exporting data
  • Data cleaning and preparation
  • Using dplyr for data manipulation
  • Filtering, selecting, and summarizing data

Module 3: Data Visualization with ggplot2

  • Introduction to data visualization principles
  • Creating static visualizations with ggplot2
  • Customizing plots (colors, labels, themes)
  • Creating multi-layered visualizations

Module 4: Exploratory Data Analysis (EDA)

  • Principles of exploratory data analysis
  • Using R to explore data distributions and relationships
  • Identifying trends and outliers
  • Documenting and interpreting findings

Module 5: Statistical Analysis

  • Introduction to descriptive and inferential statistics
  • Hypothesis testing and confidence intervals
  • Using R for t-tests, chi-squared tests, and ANOVA
  • Practical applications of statistical analysis

Module 6: Introduction to Machine Learning

  • Overview of machine learning concepts
  • Types of machine learning: supervised vs. unsupervised
  • Building a simple linear regression model in R
  • Evaluating model performance

Module 7: Advanced Machine Learning Techniques

  • Introduction to classification algorithms (e.g., logistic regression, decision trees)
  • Model evaluation techniques (confusion matrix, ROC curves)
  • Implementing models using the caret package
  • Hands-on project: Building a classification model

Module 8: Time Series Analysis

  • Introduction to time series data and its characteristics
  • Time series decomposition and forecasting
  • Using R for time series analysis
  • Practical examples and applications

Module 9: Text Mining and Natural Language Processing (NLP)

  • Overview of text mining and its applications
  • Preprocessing text data in R
  • Basic NLP techniques using R
  • Hands-on project: Analyzing text data

Module 10: Capstone Project and Course Wrap-Up

  • Hands-on project: Applying learned skills to a real-world dataset
  • Presentations of group projects
  • Course review and key takeaways
  • Next steps for continued learning in R and data science

Prerequisites

No specific prerequisites required. This course is suitable for beginners and professionals alike.

Course Administration Details

Customized Training

This training can be tailored to your institution needs and delivered at a location of your choice upon request.

Requirements

Participants need to be proficient in English.

Training Fee

The fee covers tuition, training materials, refreshments, lunch, and study visits. Participants are responsible for their own travel, visa, insurance, and personal expenses.

Certification

Upon successful completion of this course, participants will be issued with a certificate from Ideal Workplace Solutions certified by the National Industrial Training Authority (NITA) under License NO: NITA/TRN/2734.

Accommodation

Accommodation can be arranged upon request. Contact via email for reservations.

Payment

Payment should be made before the training starts, with proof of payment sent to outreach@idealworkplacesolutions.org.

For further inquiries, please contact us on details below:

Register for the Course

Select a date and location that works for you.

In-Person Training Schedules


January 2026
Date Days Venue Fee (VAT Incl.) Register
5 Jan - 16 Jan 2026 10 days Nairobi, Kenya KES 198,000 | USD 2,800 Enroll Now
5 Jan - 16 Jan 2026 10 days Cape Town, South Africa USD 7,500 Enroll Now
5 Jan - 16 Jan 2026 10 days Dubai, United Arabs Emirates USD 8,000 Enroll Now
5 Jan - 16 Jan 2026 10 days Zanzibar, Tanzania USD 4,400 Enroll Now
12 Jan - 23 Jan 2026 10 days Mombasa, Kenya KES 230,000 | USD 3,000 Enroll Now
12 Jan - 23 Jan 2026 10 days Kigali, Rwanda USD 3,800 Enroll Now
12 Jan - 23 Jan 2026 10 days Accra, Ghana USD 7,200 Enroll Now
12 Jan - 23 Jan 2026 10 days Kampala, Uganda USD 3,800 Enroll Now
19 Jan - 30 Jan 2026 10 days Dar es Salaam, Tanzania USD 4,300 Enroll Now
19 Jan - 30 Jan 2026 10 days Johannesburg, South Africa USD 6,500 Enroll Now
19 Jan - 30 Jan 2026 10 days Nakuru, Kenya KES 210,000 | USD 2,800 Enroll Now
19 Jan - 30 Jan 2026 10 days Dakar, Senegal USD 6,000 Enroll Now
26 Jan - 6 Feb 2026 10 days Pretoria, South Africa USD 6,300 Enroll Now
26 Jan - 6 Feb 2026 10 days Kisumu, Kenya KES 210,000 | USD 3,000 Enroll Now
26 Jan - 6 Feb 2026 10 days Naivasha, Kenya KES 210,000 | USD 2,800 Enroll Now
26 Jan - 6 Feb 2026 10 days Arusha, Tanzania USD 4,300 Enroll Now
5 Jan - 16 Jan 2026
10 days
Venue:
Nairobi, Kenya
Fee (VAT Incl.):
KES 198,000
USD 2,800
Enroll Now
5 Jan - 16 Jan 2026
10 days
Venue:
Cape Town, South Africa
Fee (VAT Incl.):
USD 7,500
Enroll Now
5 Jan - 16 Jan 2026
10 days
Venue:
Dubai, United Arabs Emirates
Fee (VAT Incl.):
USD 8,000
Enroll Now
5 Jan - 16 Jan 2026
10 days
Venue:
Zanzibar, Tanzania
Fee (VAT Incl.):
USD 4,400
Enroll Now
12 Jan - 23 Jan 2026
10 days
Venue:
Mombasa, Kenya
Fee (VAT Incl.):
KES 230,000
USD 3,000
Enroll Now
12 Jan - 23 Jan 2026
10 days
Venue:
Kigali, Rwanda
Fee (VAT Incl.):
USD 3,800
Enroll Now
12 Jan - 23 Jan 2026
10 days
Venue:
Accra, Ghana
Fee (VAT Incl.):
USD 7,200
Enroll Now
12 Jan - 23 Jan 2026
10 days
Venue:
Kampala, Uganda
Fee (VAT Incl.):
USD 3,800
Enroll Now
19 Jan - 30 Jan 2026
10 days
Venue:
Dar es Salaam, Tanzania
Fee (VAT Incl.):
USD 4,300
Enroll Now
19 Jan - 30 Jan 2026
10 days
Venue:
Johannesburg, South Africa
Fee (VAT Incl.):
USD 6,500
Enroll Now
19 Jan - 30 Jan 2026
10 days
Venue:
Nakuru, Kenya
Fee (VAT Incl.):
KES 210,000
USD 2,800
Enroll Now
19 Jan - 30 Jan 2026
10 days
Venue:
Dakar, Senegal
Fee (VAT Incl.):
USD 6,000
Enroll Now
26 Jan - 6 Feb 2026
10 days
Venue:
Pretoria, South Africa
Fee (VAT Incl.):
USD 6,300
Enroll Now
26 Jan - 6 Feb 2026
10 days
Venue:
Kisumu, Kenya
Fee (VAT Incl.):
KES 210,000
USD 3,000
Enroll Now
26 Jan - 6 Feb 2026
10 days
Venue:
Naivasha, Kenya
Fee (VAT Incl.):
KES 210,000
USD 2,800
Enroll Now
26 Jan - 6 Feb 2026
10 days
Venue:
Arusha, Tanzania
Fee (VAT Incl.):
USD 4,300
Enroll Now

Request Custom Training


We offer customized training solutions tailored to your organization's specific needs:

  • Training at your preferred location
  • Customized content to address your specific challenges
  • Flexible scheduling to accommodate your team
  • Cost-effective solution for training multiple employees
Limited Time
Early-bird Offer

Special pricing ends in:

-- Days
-- Hours
-- Mins
-- Secs
Recent Activity

Frequently Asked Questions

Find answers to common questions about this course

The goal is to equip you with the skills to use R for the entire data science workflow, from data manipulation and visualization to statistical modeling and reporting.
R is a powerful, open-source language with a massive community and thousands of packages specifically designed for statistical analysis, data visualization, and machine learning.
You'll master packages like dplyr for data wrangling, tidyr for data cleaning, and the entire Tidyverse ecosystem to transform and prepare your data efficiently.
You'll learn to use ggplot2 to create professional-quality, highly customizable static and interactive charts, plots, and maps that effectively communicate your insights.
The training covers how to build and interpret statistical models like linear and logistic regression and use packages to perform more advanced machine learning tasks.
Training on R Programming for Data Science

Next class starts 5 Jan 2026

Secure Your Spot
Only 5 seats remaining!
1
Ideal Workplace Solutions
Ideal Workplace Solutions
Typically replies instantly

Hi there! šŸ‘‹

How can we help you today? Are you looking for information about our training courses?

Just now